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Classification Of Pulmonary Pathology From Breath Sounds Using The Wavelet Packet Transform And An Extreme Learning Machine

Background:Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases.From this perspective,we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds.Methods:...

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Main Authors: Sundaraj, Kenneth, Palaniappan, Rajkumar, Sundaraj, Sebastian, Huliraj, N., Revadi, S. S.
Format: Article
Language:English
Published: Walter de Gruyter GmbH 2018
Subjects:
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utem.edu.my/id/eprint/22945/2/palaniappan2018-ProfK.pdf
http://eprints.utem.edu.my/id/eprint/22945/
https://www.degruyter.com/view/j/bmte.2018.63.issue-4/bmt-2016-0097/bmt-2016-0097.xml
https://www.degruyter.com/view/j/bmte.2018.63.issue-4/bmt-2016-0097/bmt-2016-0097.xml
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spelling my.utem.eprints.229452021-08-27T12:04:00Z http://eprints.utem.edu.my/id/eprint/22945/ Classification Of Pulmonary Pathology From Breath Sounds Using The Wavelet Packet Transform And An Extreme Learning Machine Sundaraj, Kenneth Palaniappan, Rajkumar Sundaraj, Sebastian Huliraj, N. Revadi, S. S. T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Background:Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases.From this perspective,we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds.Methods:Energy and entropy features were extracted from the breath sound using the wavelet packet transform.The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA).The extracted features were inputted into the ELM classifier.Results:The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%,respectively,whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%,respectively.In addition,maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features,respectively.Conclusion:The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features. Walter de Gruyter GmbH 2018-07 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/22945/2/palaniappan2018-ProfK.pdf Sundaraj, Kenneth and Palaniappan, Rajkumar and Sundaraj, Sebastian and Huliraj, N. and Revadi, S. S. (2018) Classification Of Pulmonary Pathology From Breath Sounds Using The Wavelet Packet Transform And An Extreme Learning Machine. Biomedizinische Technik, 63. pp. 383-394. ISSN 1862-278X https://www.degruyter.com/view/j/bmte.2018.63.issue-4/bmt-2016-0097/bmt-2016-0097.xml https://www.degruyter.com/view/j/bmte.2018.63.issue-4/bmt-2016-0097/bmt-2016-0097.xml
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
topic T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Sundaraj, Kenneth
Palaniappan, Rajkumar
Sundaraj, Sebastian
Huliraj, N.
Revadi, S. S.
Classification Of Pulmonary Pathology From Breath Sounds Using The Wavelet Packet Transform And An Extreme Learning Machine
description Background:Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases.From this perspective,we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds.Methods:Energy and entropy features were extracted from the breath sound using the wavelet packet transform.The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA).The extracted features were inputted into the ELM classifier.Results:The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%,respectively,whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%,respectively.In addition,maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features,respectively.Conclusion:The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.
format Article
author Sundaraj, Kenneth
Palaniappan, Rajkumar
Sundaraj, Sebastian
Huliraj, N.
Revadi, S. S.
author_facet Sundaraj, Kenneth
Palaniappan, Rajkumar
Sundaraj, Sebastian
Huliraj, N.
Revadi, S. S.
author_sort Sundaraj, Kenneth
title Classification Of Pulmonary Pathology From Breath Sounds Using The Wavelet Packet Transform And An Extreme Learning Machine
title_short Classification Of Pulmonary Pathology From Breath Sounds Using The Wavelet Packet Transform And An Extreme Learning Machine
title_full Classification Of Pulmonary Pathology From Breath Sounds Using The Wavelet Packet Transform And An Extreme Learning Machine
title_fullStr Classification Of Pulmonary Pathology From Breath Sounds Using The Wavelet Packet Transform And An Extreme Learning Machine
title_full_unstemmed Classification Of Pulmonary Pathology From Breath Sounds Using The Wavelet Packet Transform And An Extreme Learning Machine
title_sort classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine
publisher Walter de Gruyter GmbH
publishDate 2018
url http://eprints.utem.edu.my/id/eprint/22945/2/palaniappan2018-ProfK.pdf
http://eprints.utem.edu.my/id/eprint/22945/
https://www.degruyter.com/view/j/bmte.2018.63.issue-4/bmt-2016-0097/bmt-2016-0097.xml
https://www.degruyter.com/view/j/bmte.2018.63.issue-4/bmt-2016-0097/bmt-2016-0097.xml
_version_ 1710679440780951552
score 13.211869

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